mwptoolkit.module.Layer.tree_layers

class mwptoolkit.module.Layer.tree_layers.DQN(input_size, embedding_size, hidden_size, output_size, dropout_ratio)

Bases: Module

Initializes internal Module state, shared by both nn.Module and ScriptModule.

forward(inputs)

Defines the computation performed at every call.

Should be overridden by all subclasses.

Note

Although the recipe for forward pass needs to be defined within this function, one should call the Module instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.

play_one(inputs)

training: bool

class mwptoolkit.module.Layer.tree_layers.Dec_LSTM(embedding_size, hidden_size, dropout_ratio)

Bases: Module

Initializes internal Module state, shared by both nn.Module and ScriptModule.

forward(x, prev_c, prev_h, parent_h, sibling_state)

Defines the computation performed at every call.

Should be overridden by all subclasses.

Note

Although the recipe for forward pass needs to be defined within this function, one should call the Module instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.

training: bool

class mwptoolkit.module.Layer.tree_layers.DecomposeModel(hidden_size, dropout, device)

Bases: Module

Initializes internal Module state, shared by both nn.Module and ScriptModule.

forward(node_stacks, tree_stacks, nodes_context, labels_embedding, pad_node=True)

Defines the computation performed at every call.

Should be overridden by all subclasses.

Note

Although the recipe for forward pass needs to be defined within this function, one should call the Module instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.

training: bool

class mwptoolkit.module.Layer.tree_layers.GateNN(hidden_size, input1_size, input2_size=0, dropout=0.4, single_layer=False)

Bases: Module

Initializes internal Module state, shared by both nn.Module and ScriptModule.

forward(hidden, input1, input2=None)

Defines the computation performed at every call.

Should be overridden by all subclasses.

Note

Although the recipe for forward pass needs to be defined within this function, one should call the Module instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.

training: bool

class mwptoolkit.module.Layer.tree_layers.GenerateNode(hidden_size, op_nums, embedding_size, dropout=0.5)

Bases: Module

Initializes internal Module state, shared by both nn.Module and ScriptModule.

forward(node_embedding, node_label, current_context)

Parameters

• node_embedding (torch.Tensor) – node embedding, shape [batch_size, hidden_size].

• node_label (torch.Tensor) – representation of node label, shape [batch_size, embedding_size].

• current_context (torch.Tensor) – current context, shape [batch_size, hidden_size].

Returns

l_child, representation of left child, shape [batch_size, hidden_size]. r_child, representation of right child, shape [batch_size, hidden_size]. node_label_, representation of node label, shape [batch_size, embedding_size].

Return type

tuple(torch.Tensor, torch.Tensor, torch.Tensor)

training: bool

class mwptoolkit.module.Layer.tree_layers.Merge(hidden_size, embedding_size, dropout=0.5)

Bases: Module

Initializes internal Module state, shared by both nn.Module and ScriptModule.

forward(node_embedding, sub_tree_1, sub_tree_2)

Parameters

• node_embedding (torch.Tensor) – node embedding, shape [1, embedding_size].

• sub_tree_1 (torch.Tensor) – representation of sub tree 1, shape [1, hidden_size].

• sub_tree_2 (torch.Tensor) – representation of sub tree 2, shape [1, hidden_size].

Returns

representation of merged tree, shape [1, hidden_size].

Return type

torch.Tensor

training: bool

class mwptoolkit.module.Layer.tree_layers.Node(node_value, isleaf=True)

Bases: object

set_left_node(node)

set_right_node(node)

class mwptoolkit.module.Layer.tree_layers.NodeEmbeddingLayer(op_nums, embedding_size)

Bases: Module

Initializes internal Module state, shared by both nn.Module and ScriptModule.

forward(node_embedding, node_label, current_context)

Parameters

• node_embedding (torch.Tensor) – node embedding, shape [batch_size, num_directions * hidden_size].

• node_label (torch.Tensor) – shape [batch_size].

Returns

l_child, representation of left child, shape [batch_size, num_directions * hidden_size]. r_child, representation of right child, shape [batch_size, num_directions * hidden_size]. node_label_, representation of node label, shape [batch_size, embedding_size].

Return type

tuple(torch.Tensor, torch.Tensor, torch.Tensor)

training: bool

class mwptoolkit.module.Layer.tree_layers.NodeEmbeddingNode(node_hidden, node_context=None, label_embedding=None)

Bases: object

class mwptoolkit.module.Layer.tree_layers.NodeGenerater(hidden_size, op_nums, embedding_size, dropout=0.5)

Bases: Module

Initializes internal Module state, shared by both nn.Module and ScriptModule.

forward(node_embedding, node_label, current_context)

Defines the computation performed at every call.

Should be overridden by all subclasses.

Note

Although the recipe for forward pass needs to be defined within this function, one should call the Module instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.

training: bool

class mwptoolkit.module.Layer.tree_layers.Prediction(hidden_size, op_nums, input_size, dropout=0.5)

Bases: Module

Initializes internal Module state, shared by both nn.Module and ScriptModule.

forward(node_stacks, left_childs, encoder_outputs, num_pades, padding_hidden, seq_mask, mask_nums)

Parameters

• node_stacks (list) – node stacks.

• left_childs (list) – representation of left childs.

• encoder_outputs (torch.Tensor) – output from encoder, shape [sequence_length, batch_size, hidden_size].

• num_pades (torch.Tensor) – number representation, shape [batch_size, number_size, hidden_size].

• padding_hidden (torch.Tensor) – padding hidden, shape [1,hidden_size].

• seq_mask (torch.BoolTensor) – sequence mask, shape [batch_size, sequence_length].

• mask_nums (torch.BoolTensor) – number mask, shape [batch_size, number_size].

Returns

num_score, number score, shape [batch_size, number_size]. op, operator score, shape [batch_size, operator_size]. current_node, current node representation, shape [batch_size, 1, hidden_size]. current_context, current context representation, shape [batch_size, 1, hidden_size]. embedding_weight, embedding weight, shape [batch_size, number_size, hidden_size].

Return type

tuple(torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor)

training: bool

class mwptoolkit.module.Layer.tree_layers.RecursiveNN(emb_size, op_size, op_list)

Bases: Module

Initializes internal Module state, shared by both nn.Module and ScriptModule.

RecurCell(combine_emb)

forward(expression_tree, num_embedding, look_up, out_idx2symbol)

Defines the computation performed at every call.

Should be overridden by all subclasses.

Note

Although the recipe for forward pass needs to be defined within this function, one should call the Module instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.

leaf_emb(node, num_embed, look_up)

test(expression_tree, num_embedding, look_up, out_idx2symbol)

test_traverse(node)

training: bool

traverse(node)

class mwptoolkit.module.Layer.tree_layers.Score(input_size, hidden_size)

Bases: Module

Initializes internal Module state, shared by both nn.Module and ScriptModule.

forward(hidden, num_embeddings, num_mask=None)

Parameters

• hidden (torch.Tensor) – hidden representation, shape [batch_size, 1, hidden_size + input_size].

• num_embeddings (torch.Tensor) – number embedding, shape [batch_size, number_size, hidden_size].

• num_mask (torch.BoolTensor) – number mask, shape [batch_size, number_size].

Returns

shape [batch_size, number_size].

Return type

score (torch.Tensor)

training: bool

class mwptoolkit.module.Layer.tree_layers.ScoreModel(hidden_size)

Bases: Module

Initializes internal Module state, shared by both nn.Module and ScriptModule.

forward(hidden, context, token_embeddings)

Defines the computation performed at every call.

Should be overridden by all subclasses.

Note

Although the recipe for forward pass needs to be defined within this function, one should call the Module instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.

training: bool

class mwptoolkit.module.Layer.tree_layers.SemanticAlignmentModule(encoder_hidden_size, decoder_hidden_size, hidden_size, batch_first=False)

Bases: Module

Initializes internal Module state, shared by both nn.Module and ScriptModule.

forward(decoder_hidden, encoder_outputs)

Defines the computation performed at every call.

Should be overridden by all subclasses.

Note

Although the recipe for forward pass needs to be defined within this function, one should call the Module instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.

training: bool

class mwptoolkit.module.Layer.tree_layers.SubTreeMerger(hidden_size, embedding_size, dropout=0.5)

Bases: Module

Initializes internal Module state, shared by both nn.Module and ScriptModule.

forward(node_embedding, sub_tree_1, sub_tree_2)

Defines the computation performed at every call.

Should be overridden by all subclasses.

Note

Although the recipe for forward pass needs to be defined within this function, one should call the Module instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.

training: bool

class mwptoolkit.module.Layer.tree_layers.TreeAttention(input_size, hidden_size)

Bases: Module

Initializes internal Module state, shared by both nn.Module and ScriptModule.

forward(hidden, encoder_outputs, seq_mask=None)

Parameters

• hidden (torch.Tensor) – hidden representation, shape [1, batch_size, hidden_size]

• encoder_outputs (torch.Tensor) – output from encoder, shape [sequence_length, batch_size, hidden_size].

• seq_mask (torch.Tensor) – sequence mask, shape [batch_size, sequence_length].

Returns

attention energies, shape [batch_size, 1, sequence_length].

Return type

attn_energies (torch.Tensor)

training: bool

class mwptoolkit.module.Layer.tree_layers.TreeEmbedding(embedding, terminal=False)

Bases: object

class mwptoolkit.module.Layer.tree_layers.TreeEmbeddingModel(hidden_size, op_set, dropout=0.4)

Bases: Module

Initializes internal Module state, shared by both nn.Module and ScriptModule.

forward(class_embedding, tree_stacks, embed_node_index)

Defines the computation performed at every call.

Should be overridden by all subclasses.

Note

Although the recipe for forward pass needs to be defined within this function, one should call the Module instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.

merge(op_embedding, left_embedding, right_embedding)

training: bool

class mwptoolkit.module.Layer.tree_layers.TreeNode(embedding, left_flag=False, terminal=False)

Bases: object